These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

197 related articles for article (PubMed ID: 23815611)

  • 1. Integrating peptides' sequence and energy of contact residues information improves prediction of peptide and HLA-I binding with unknown alleles.
    Luo F; Gao Y; Zhu Y; Liu J
    BMC Bioinformatics; 2013; 14 Suppl 8(Suppl 8):S1. PubMed ID: 23815611
    [TBL] [Abstract][Full Text] [Related]  

  • 2. NetMHCpan, a method for MHC class I binding prediction beyond humans.
    Hoof I; Peters B; Sidney J; Pedersen LE; Sette A; Lund O; Buus S; Nielsen M
    Immunogenetics; 2009 Jan; 61(1):1-13. PubMed ID: 19002680
    [TBL] [Abstract][Full Text] [Related]  

  • 3. DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction.
    Liu Z; Cui Y; Xiong Z; Nasiri A; Zhang A; Hu J
    Sci Rep; 2019 Jan; 9(1):794. PubMed ID: 30692623
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.
    Luo H; Ye H; Ng H; Shi L; Tong W; Mattes W; Mendrick D; Hong H
    BMC Bioinformatics; 2015; 16 Suppl 13(Suppl 13):S9. PubMed ID: 26424483
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods.
    Zhang H; Lundegaard C; Nielsen M
    Bioinformatics; 2009 Jan; 25(1):83-9. PubMed ID: 18996943
    [TBL] [Abstract][Full Text] [Related]  

  • 6. PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity.
    Liu G; Li D; Li Z; Qiu S; Li W; Chao CC; Yang N; Li H; Cheng Z; Song X; Cheng L; Zhang X; Wang J; Yang H; Ma K; Hou Y; Li B
    Gigascience; 2017 May; 6(5):1-11. PubMed ID: 28327987
    [TBL] [Abstract][Full Text] [Related]  

  • 7. PromPDD, a web-based tool for the prediction, deciphering and design of promiscuous peptides that bind to HLA class I molecules.
    Zhang S; Chen J; Hong P; Li J; Tian Y; Wu Y; Wang S
    J Immunol Methods; 2020 Jan; 476():112685. PubMed ID: 31678214
    [TBL] [Abstract][Full Text] [Related]  

  • 8. MATHLA: a robust framework for HLA-peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism.
    Ye Y; Wang J; Xu Y; Wang Y; Pan Y; Song Q; Liu X; Wan J
    BMC Bioinformatics; 2021 Jan; 22(1):7. PubMed ID: 33407098
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Improving the prediction of HLA class I-binding peptides using a supertype-based method.
    Wang S; Bai Z; Han J; Tian Y; Shang X; Wang L; Li J; Wu Y
    J Immunol Methods; 2014 Mar; 405():109-20. PubMed ID: 24508661
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development and validation of an epitope prediction tool for swine (PigMatrix) based on the pocket profile method.
    Gutiérrez AH; Martin WD; Bailey-Kellogg C; Terry F; Moise L; De Groot AS
    BMC Bioinformatics; 2015 Sep; 16():290. PubMed ID: 26370412
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Anti-HLA-E mAb 3D12 mimics MEM-E/02 in binding to HLA-B and HLA-C alleles: Web-tools validate the immunogenic epitopes of HLA-E recognized by the antibodies.
    Ravindranath MH; Pham T; El-Awar N; Kaneku H; Terasaki PI
    Mol Immunol; 2011 Jan; 48(4):423-30. PubMed ID: 21145594
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.
    Mei S; Li F; Leier A; Marquez-Lago TT; Giam K; Croft NP; Akutsu T; Smith AI; Li J; Rossjohn J; Purcell AW; Song J
    Brief Bioinform; 2020 Jul; 21(4):1119-1135. PubMed ID: 31204427
    [TBL] [Abstract][Full Text] [Related]  

  • 13. sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.
    Luo H; Ye H; Ng HW; Sakkiah S; Mendrick DL; Hong H
    Sci Rep; 2016 Aug; 6():32115. PubMed ID: 27558848
    [TBL] [Abstract][Full Text] [Related]  

  • 14. TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules.
    Zhang L; Chen Y; Wong HS; Zhou S; Mamitsuka H; Zhu S
    PLoS One; 2012; 7(2):e30483. PubMed ID: 22383964
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests.
    Abella JR; Antunes DA; Clementi C; Kavraki LE
    Front Immunol; 2020; 11():1583. PubMed ID: 32793224
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.
    Carrasco Pro S; Zimic M; Nielsen M
    Tissue Antigens; 2014 Feb; 83(2):94-100. PubMed ID: 24447175
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.
    Han Y; Kim D
    BMC Bioinformatics; 2017 Dec; 18(1):585. PubMed ID: 29281985
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Sequence conservation analysis and in silico human leukocyte antigen-peptide binding predictions for the Mtb72F and M72 tuberculosis candidate vaccine antigens.
    Mortier MC; Jongert E; Mettens P; Ruelle JL
    BMC Immunol; 2015 Oct; 16():63. PubMed ID: 26493839
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of epitopes using neural network based methods.
    Lundegaard C; Lund O; Nielsen M
    J Immunol Methods; 2011 Nov; 374(1-2):26-34. PubMed ID: 21047511
    [TBL] [Abstract][Full Text] [Related]  

  • 20. MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes.
    Bordner AJ; Mittelmann HD
    BMC Bioinformatics; 2010 Sep; 11():482. PubMed ID: 20868497
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.